Re: Concerns to software freedom when packaging deep-learning based appications.
Ian Jackson <ijackson@chiark.greenend.org.uk> writes:
> Things in Debian main shoudl be buildable *from source* using Debian
> main. In the case of a pretrained neural network, the source code is
> the training data.
>
> In fact, they are probably not redistributable unless all the training
> data is supplied, since the GPL's definition of "source code" is the
> "preferred form for modification". For a pretrained neural network
> that is the training data.
The above (with which I agree completely) puts me in mind of this
article from O'Reilly Media:
Compilers automated the process of writing machine code. Scripting
languages automate many mundane tasks by gluing together larger,
more complex programs. Software testing tools, automated deployment
tools, containers, and container orchestration systems are all tools
for automating the process of developing, deploying, and managing
software systems. None of these take advantage of machine learning,
but that is certainly the next step.
[…] We’ve seen research suggesting that neural networks can create
new programs by combining existing modules. The system is trained
using execution traces from other programs. […]
Thinking more systematically, Peter Norvig has argued that machine
learning can be used to generate short programs (but not long ones)
from training data; to optimize small parts of larger programs, but
not the entire program; and possibly to (with the help of humans) be
better tutors to beginning programmers.
<URL:https://www.oreilly.com/ideas/what-machine-learning-means-for-software-development>
This thread will be just one of many we will need to have, to determine
how best to preserve software freedom as the practice of developing
software changes fundamentally.
--
\ “I always had a repulsive need to be something more than |
`\ human.” —David Bowie |
_o__) |
Ben Finney
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